ArXiv

Reversible Unlearnable Examples: Towards the Copyright Protection in Deep Learning Era

2026-08-06 · 16:04 UTC ·Binze Wang, Jinyu Tian, Xingrun Wang... ·1 min read
Authors
Binze Wang, Jinyu Tian, Xingrun Wang...
Categories
cs.CR, cs.CV
arXiv
https://arxiv.org/abs/2608.06211v1
PDF
https://arxiv.org/pdf/2608.06211v1

Brief

The paper addresses copyright threats from illegal model training and malicious data leakage by introducing reversible unlearnable examples: perturbations optimized to minimize mutual information between inputs and model outputs so learned features are uncorrelated, plus a dual-extractor watermarking scheme to ensure watermark recovery. Experiments on ImageNet, CIFAR-10, and Pets (arXiv:2608.06211v1, 2026-08-06) report comprehensive protection; code is on GitHub.

Why it matters

Proposes "Reversible Unlearnable Examples": perturbations that protect images from unauthorized training by minimizing mutual information between model input and output so models learn uncorrelated features (Binze Wang et al., arXiv:2608.06211v1, 2026-08-06).

Key details

  • Adds a dual watermark-extraction strategy (two distinct extractors) to avoid interference from unlearnable perturbations; evaluated on ImageNet, CIFAR-10, and Pets and code is published at https://github.com/Yeah21/ReversibleUnlearnableExamples.
Source evidence

Abstract

Significant advancements in deep learning have been made possible by the utilization of large datasets, underscoring the critical importance of copyright protection. Adding meticulously designed perturbations to examples, making them unlearnable has become a crucial approach for safeguarding data copyright. Existing methods for creating unlearnable examples overlook the risk of data leakage, which can threaten data ownership. Thus, copyright protection in deep learning faces two main threats: illegal model training and malicious data leakage. We investigate that these two threats cannot be solved by straightforwardly combining existing availability attacks and watermarking techniques as their negative interaction effects. Therefore, in this paper, we propose a novel copyright protection mechanism for the aforementioned security concerns. Considering that the prevention of unauthorized model training requires powerful generalizability of unlearnable perturbations, we generate perturbations to induce the model to learn uncorrelated features of input images. It works by minimizing the mutual information of the input and output of the model. On the other hand, to eliminate the side impact of unlearnable perturbations on the watermark extraction, we design a dual extraction strategy by using two distinct watermark extractors. Extensive experiments on the image datasets {ImageNet, CIFAR10, and Pets} show that our proposed method could provide comprehensive copyright protection to images. The code is available at {https://github.com/Yeah21/ReversibleUnlearnableExamples}.